Senior AI Full-Stack Engineer (Founding Team)

Senior / Staff Software Engineer – AI Agents & Data Platforms (Founding role)
Location: Remote
Employment Type: Full-Time
Experience: 8+ years (Senior) / 10+ years (Staff)
Company Description
DataFramer helps teams ship better AI faster by providing modern tools and infrastructure tailored to AI development workflows. Backed by Bessemer Venture Partners, DataFramer is a well-funded early-stage startup building at the intersection of AI, data engineering, and cloud-native software.
DataFramer is a Databricks Validated Partner, and its product is available on AWS Marketplace, enabling enterprise customers to adopt and scale AI solutions.
We're looking for experienced engineers who want to help shape the next generation of AI-native applications, autonomous agents, and intelligent data workflows.
Role Description
We are seeking a Senior / Staff AI Full-Stack Engineer to join our founding engineering team and play a critical role in architecting and building DataFramer's AI platform.
This is a hands-on engineering role for someone with deep full-stack expertise and proven production experience building AI agents, LLM-powered applications, tool-calling systems, data pipelines, and agentic workflows.
You will design and develop AI-powered applications, build scalable backend infrastructure, integrate with modern data warehouses and data platforms, and create intuitive interfaces that enable customers to build, orchestrate, and deploy AI workflows.
As an early engineering leader, you will own major technical decisions, drive system architecture, and work directly with the founding team to shape the product and engineering direction.
Key Responsibilities
Build AI Agents & Agentic Systems: Design and deploy production-grade AI agents capable of tool calling, function execution, multi-step reasoning, and autonomous task completion.
Develop AI Workflows: Architect LLM-powered workflows, agent orchestration systems, and integrations with external APIs, databases, data warehouses, and enterprise tools.
Build Data Integrations & Pipelines: Design and develop scalable data ingestion, transformation, and processing pipelines connecting enterprise data sources, data warehouses, and AI applications.
Integrate Data Warehouses: Build integrations with platforms such as Databricks, Snowflake, BigQuery, and Redshift, enabling AI agents and applications to securely access, query, and process enterprise data.
Own Full-Stack Development: Build end-to-end product features, from backend services and APIs to modern, responsive frontend applications.
Design Scalable AI Infrastructure: Develop reliable backend systems for LLM inference, tool execution, asynchronous processing, data access, and workflow orchestration.
Implement Advanced LLM Capabilities: Work with structured outputs, function calling, RAG, context management, and agent memory.
Improve AI Reliability: Implement evaluation frameworks, tracing, observability, error handling, and safeguards for production AI applications.
Drive Technical Architecture: Lead architectural decisions, establish engineering standards, and ensure scalability, security, and maintainability.
Provide Technical Leadership: Mentor engineers, review system designs, drive code quality, and influence the company's long-term technical strategy.
Ship Quickly and Independently: Own features from concept through deployment in a fast-moving startup environment.
Required QualificationsSoftware Engineering & Technical Leadership
8+ years of professional software engineering experience for Senior candidates, or 10+ years for Staff-level candidates, with a strong track record of designing and shipping production software.
Strong full-stack development experience with React, TypeScript, JavaScript, and backend languages such as Python or Node.js.
Extensive experience designing REST APIs, backend services, distributed systems, and scalable application architectures.
Strong understanding of databases, asynchronous processing, cloud infrastructure, and production system design.
Hands-on experience deploying and operating applications on AWS or comparable cloud platforms.
Experience with CI/CD, automated testing, monitoring, and production debugging.
Demonstrated ability to own complex technical projects from architecture through production deployment.
Ability to work autonomously, make architectural decisions, and deliver products in a fast-paced startup environment.
AI Agents & LLM Engineering — Required
2+ years of hands-on experience building AI/LLM-powered applications, including production experience developing AI agents and agentic workflows.
Mandatory experience implementing LLM tool calling and function calling, enabling AI agents to interact with APIs, execute tools, retrieve data, and perform multi-step tasks.
Experience designing and implementing AI workflow orchestration, including multi-step execution, state management, retries, error handling, and coordination between AI models and external systems.
Experience working with LLM APIs such as OpenAI, Anthropic, or similar providers.
Experience implementing structured outputs, context handling, and reliable tool execution.
Ability to debug, evaluate, and optimize agent behavior, latency, accuracy, and reliability in production.
Data Engineering, Data Warehouses & Pipelines — Required
Hands-on experience working with modern data warehouses and lakehouse platforms such as Databricks, Snowflake, BigQuery, Amazon Redshift, or similar technologies.
Experience designing, building, and maintaining production data pipelines, including data ingestion, transformation, orchestration, and ETL/ELT workflows.
Strong SQL proficiency, including working with complex queries, data models, and large datasets.
Experience integrating applications and backend services with data warehouses, relational databases, and enterprise data sources.
Understanding of batch processing, asynchronous data workflows, data quality, and pipeline reliability.
Experience exposing data through APIs or services for consumption by AI applications, agents, or downstream systems.
Familiarity with cloud-based data architectures, data security, access controls, and performance optimization.
Preferred Qualifications
Experience with agent frameworks such as LangGraph, LangChain, CrewAI, OpenAI Agents SDK, or similar orchestration frameworks.
Experience building multi-agent systems, long-running AI workflows, and human-in-the-loop applications.
Familiarity with MCP (Model Context Protocol), agent tool integrations, and standards for agent interoperability.
Experience with RAG architectures, vector databases, embeddings, and semantic search.
Experience building AI evaluation pipelines, LLM observability, tracing, and monitoring.
Experience with Apache Spark, PySpark, Delta Lake, or Databricks-native data engineering workflows.
Familiarity with data pipeline and orchestration frameworks such as Apache Airflow, Dagster, dbt, or similar tools.
Experience working with streaming data platforms such as Kafka or Kinesis.
Experience building AI agents that query, analyze, transform, or take actions on enterprise data.
Experience building enterprise SaaS products, developer tools, AI infrastructure, or workflow automation platforms.
Previous experience at an early-stage AI startup or as a founding engineer.
Demonstrated experience leading architecture decisions, mentoring engineers, and scaling engineering systems.
What We're Looking For
We are looking for a senior technical builder who has already shipped real AI agents, tool-calling systems, and AI-powered workflows into production, not someone whose experience is limited to basic LLM API integrations, prompt engineering, or chatbot prototypes.
The ideal candidate brings together three core areas of expertise:
AI & Agentic Systems: Production experience building AI agents, tool-calling systems, and complex LLM-powered workflows.
Full-Stack & Backend Engineering: Strong architecture and engineering skills across scalable backend services, APIs, and modern frontend applications.
Data Engineering & Infrastructure: Hands-on experience with data warehouses, SQL, data pipelines, and integrating enterprise data with AI-powered applications.
You should be comfortable working across these areas, solving ambiguous technical problems, making architectural tradeoffs, and transforming rapidly evolving AI capabilities into reliable, production-ready products.
This is an opportunity to join the founding engineering team, own foundational parts of the platform, and help define how enterprises build and operate AI applications.

Work arrangement
Yes